Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms.

Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human-computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model's quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research.

End-to-end multimodal system for depression detection from online recordings.

Depression is one of the most occurring civilizational diseases. In this paper, we propose a new approach for detecting depression through the analysis of social media content using face analysis, emotion recognition neural networks, and speech processing. We utilized audio-visual analysis and acquired more than 605 features in the time domain. Those are fed to machine learning and deep learning models for depression classification. Our approach outperforms the other state-of-the-art models, achieving the F1-score 0.77. The results have the potential to provide valuable insights for mental health professionals, offer early detection and intervention, and serve as a resource for individuals seeking help with their mental health. This study enables real-time analysis and represents a significant advancement in mental health and technology and has the potential to impact society.Clinical relevance-The system aims to provide a fast and accurate way to detect depression in individuals through online recordings. The use of multimodal information (e.g. audio, image) enhances the performance of the non-verbal behavioral analysis. The end-to-end system reduces the need for manual analysis by mental health professionals and increases the efficiency of depression screening. The system can potentially help identify individuals who are at risk for depression, enabling early intervention and treatment. The results from the system can complement traditional assessments and support mental health professionals in making a diagnosis. The system can be used in real-time processing, f.e. during online calls, and provide objective measurements summarizing the overall behavior based on computer vision and audio analysis.